Data Understanding Survey: Pursuing Improved Dataset Characterization Via Tensor-based Methods
This is an incremental survey advocating for tensor-based techniques to enhance dataset understanding in machine learning and data analytics.
The paper surveys conventional dataset characterization methods and their limitations, proposing tensor-based methods as a more robust alternative to improve interpretability and insights for complex datasets.
In the evolving domains of Machine Learning and Data Analytics, existing dataset characterization methods such as statistical, structural, and model-based analyses often fail to deliver the deep understanding and insights essential for innovation and explainability. This work surveys the current state-of-the-art conventional data analytic techniques and examines their limitations, and discusses a variety of tensor-based methods and how these may provide a more robust alternative to traditional statistical, structural, and model-based dataset characterization techniques. Through examples, we illustrate how tensor methods unveil nuanced data characteristics, offering enhanced interpretability and actionable intelligence. We advocate for the adoption of tensor-based characterization, promising a leap forward in understanding complex datasets and paving the way for intelligent, explainable data-driven discoveries.